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| Home > Publications database > Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant > print |
| 001 | 1022048 | ||
| 005 | 20240403082805.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS52108.2023.10281579 |2 doi |
| 024 | 7 | _ | |a WOS:001098971607156 |2 WOS |
| 037 | _ | _ | |a FZJ-2024-01185 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Pato, Miguel |0 P:(DE-HGF)0 |b 0 |
| 111 | 2 | _ | |a IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium |c Pasadena |d 2023-07-16 - 2023-07-21 |w CA |
| 245 | _ | _ | |a Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant |
| 260 | _ | _ | |c 2023 |b IEEE |
| 300 | _ | _ | |a 7563-7566 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a In many remote sensing applications the measured radi-ance needs to be corrected for atmospheric effects to studysurface properties such as reflectance, temperature or emis-sion features. The correction often applies radiative transferto simulate atmospheric propagation, a time-consuming stepusually done offline. In principle, an efficient machine learn-ing (ML) model can accelerate the simulation step. This is thegoal pursued here in the context of solar-induced fluorescence(SIF) emitted by vegetation around the O2-A band using thespaceborne DESIS and airborne HyPlant spectrometers. Wepresent an ML simulator of at-sensor radiances trained onsynthetic spectra and describe its performance in detail. Thesimulator is fast and accurate, constituting a promising alter-native to a full-fledged, lengthy radiative transfer code for SIFretrieval in the O2-A band with DESIS and HyPlant.Index Terms— solar-induced fluorescence, hyperspectralsensors, radiative transfer, machine learning |
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| 773 | _ | _ | |a 10.1109/IGARSS52108.2023.10281579 |
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